School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China.
Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Quanzhou 362000, China.
Sensors (Basel). 2023 Apr 10;23(8):3869. doi: 10.3390/s23083869.
There are some irregular and disordered noise points in large-scale point clouds, and the accuracy of existing large-scale point cloud classification methods still needs further improvement. This paper proposes a network named MFTR-Net, which considers the local point cloud's eigenvalue calculation. The eigenvalues of 3D point cloud data and the 2D eigenvalues of projected point clouds on different planes are calculated to express the local feature relationship between adjacent point clouds. A regular point cloud feature image is constructed and inputs into the designed convolutional neural network. The network adds TargetDrop to be more robust. The experimental result shows that our methods can learn more high-dimensional feature information, further improving point cloud classification, and our approach can achieve 98.0% accuracy with the Oakland 3D dataset.
大规模点云中存在一些不规则和无序的噪声点，现有的大规模点云分类方法的准确性仍有待进一步提高。本文提出了一种名为 MFTR-Net 的网络，该网络考虑了局部点云的特征值计算。计算 3D 点云数据的特征值和不同平面上投影点云的 2D 特征值，以表示相邻点云之间的局部特征关系。构建规则点云特征图像，并将其输入到设计的卷积神经网络中。该网络添加了 TargetDrop 以提高鲁棒性。实验结果表明，我们的方法可以学习更多的高维特征信息，进一步提高点云分类的准确性，我们的方法可以在 Oakland 3D 数据集上实现 98.0%的准确率。
